Python/Numpy:将布尔列表转换为unsigned int [英] Python/Numpy: Convert list of bools to unsigned int

问题描述

1. 最快(或最"Pythonic")转换方式是什么

1. What is the fastest (or most "Pythonic") way to convert

x = [False, False, True, True]

变成12? (如果有这种方法.)

into 12? (If there is such a way.)

如果x是bool的numpy.array怎么办?有特殊的命令吗?

What if x were instead a numpy.array of bools? Is there a special command for that?

我有一个大型的n×n布尔数组，其中每个n元素行代表一个高维特征向量的单个低维哈希. (在上面的示例中，n =4.)我想知道答案，以便尽可能地压缩我的数据.谢谢.

I have a large m-by-n array of booleans, where each n-element row represents a single low-dimensional hash of a high-dimensional feature vector. (In the example above, n = 4.) I would like to know the answer in order to compress my data as much as possible. Thank you.

编辑:谢谢您的答复！使用以下测试代码

Edit: Thank you for the responses! Using the following test code,

t = 0
for iter in range(500):
    B = scipy.signbit(scipy.randn(1000,20))
    for b in B:
        t0 = time.clock()
        # test code here
        t1 = time.clock()
        t += (t1-t0)
print t

...这是我的Thinkpad笔记本电脑上的运行时:

...here were the runtimes on my Thinkpad laptop:

• 我的答案: 4.26秒
• Sven Marnach 1 :7.88
• Emil H : 8.51
• Sven Marnach 2 :8.72
• delnan :10.14
• liori :53.49

• My answer: 4.26 sec
• Sven Marnach 1: 7.88
• Emil H: 8.51
• Sven Marnach 2: 8.72
• delnan: 10.14
• liori: 53.49

当然，我欢迎任何可能确认或反驳我的数据的独立测试！

Of course, I welcome any independent tests that may confirm or refute my data!

编辑:在下面的回答中，将int(j)更改为简单的j仍然可行，但运行速度慢了六倍！然后，如果使用int投掷布尔值，则其他答案可能会变得更快.但是我太懒了，无法再次测试所有内容.

Edit: In my answer below, changing int(j) to simply j still works, but runs six times as slow! Then perhaps the other answers would become faster if the bool was casted using int. But I'm too lazy to test everything again.

编辑:liori在此处发布了独立测试的结果.

Edit: liori posted results of independent tests here.

推荐答案

从各种其他答案中得出各种想法，这是另一种方法:

Taking various ideas from various other answers, here's another way to do it:

sum(1<<i for i, b in enumerate(x) if b)

在我的测试中，这是非常快的-即使它像疯了一样溢出，也要使用numpy方法处理大量位.我使用liori的测试模块进行测试.我建议的更改使Steve的方法快了一点.但是，如果一次需要进行很多这类转换(且位数不太多)，我敢打赌numpy会更快.

It is quite fast in my tests - right up with the numpy method for large number of bits even though it overflows like crazy. I used liori's testing module for testing. Steve's method, with the change I suggested, is just barely faster. However, if a lot of these sorts of conversions need to be done at a time (and with not too many bits), I'm betting that numpy will be faster.

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